5 papers
Compound p-values for competition-based testing
Junu Lee, Rina Foygel Barber, Richard J. Samworth +1
Standard competition-based multiple testing methods -- such as the knockoff filter and the target-decoy procedure -- are typically formulated as head-to-head competitions between a…
Power of masking methods for adaptive testing in a multivariate normal means problem
Abhinav Chakraborty, Junu Lee, Eugene Katsevich
Many large-scale testing procedures learn signal structure from the data to boost power. Direct data reuse can inflate Type-I error ("double dipping"), so a common remedy is maskin…
Full-conformal novelty detection
Junu Lee, Ilia Popov, Zhimei Ren
This paper presents a powerful methodology for flexible full-data nonparametric novelty detection that offers distribution-free false discovery rate (FDR) control guarantees. Build…
A general condition for bias attenuation by a nondifferentially mismeasured confounder
Jeffrey Zhang, Junu Lee
In real-world studies, the collected confounders may suffer from measurement error. Although mismeasurement of confounders is typically unintentional -- originating from sources su…
Boosting e-BH via conditional calibration
Junu Lee, Zhimei Ren
The e-BH procedure is an e-value-based multiple testing procedure that provably controls the false discovery rate (FDR) under any dependence structure between the e-values. Despite…